The purpose of sentiment analysis is to detect an opinion or polarity in text data. We can apply such an analysis to detect negative sentiment, which represents the anomalous activities in operating system (OS) logs. Existing methods involve manual searching, predefined rules, or traditional machine learning techniques to detect such suspicious events. In this article, we propose a novel deep learning-based sentiment analysis technique to check whether there are anomalous activities in OS logs. Log messages are modeled as sentences and we identify the sentiments using the gated recurrent unit (GRU) networks. OS log datasets inherently have a class imbalance in the sense that the number of negative sentiment is much lower than that of the number of positive ones. In order to address the class imbalance, we build a GRU layer on top of a class imbalance solver using the Tomek link method. Experimental results demonstrate that the proposed method can detect anomalous events in OS logs with an overall F1 and accuracy of 99.84 and 99.93 percent, respectively.

A forensic investigator creates a timeline from a forensic disk image after an occurrence of a security incident. This procedure aims to acquire the time for all events identified from the investigated artifacts. An investigator usually looks for events of interest by manually searching the timeline. One of the sources from which to build a timeline is log files, and these events are often found in log messages. In this paper, we propose a sentiment analysis technique to automatically extract events of interest from log messages in the forensic timeline. We use a deep learning technique with a context and content attention model to identify aspect terms and the corresponding sentiments in the forensic timeline. Terms with negative sentiments indicate events of interest and are highlighted in the timeline. Therefore, the investigator can quickly examine the events and other activities recorded within the surrounding time frame. Experimental results on four public forensic case studies show that the proposed method achieves 98.43% and 99.64% for the F1 score and accuracy, respectively.